The ML–EM algorithm in continuum: sparse measure solutions

DOI10.1088/1361-6420/ab6d55zbMath1436.62392arXiv1909.01966OpenAlexW3001809572MaRDI QIDQ5220313

Camille Pouchol, Olivier Verdier

Publication date: 16 March 2020

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.01966

zbMATH Keywords

inverse problemsmaximum likelihoodKullback-Leibler divergencepositron emission tomographyRichardson-Lucyexpectation-maximisation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Image analysis in multivariate analysis (62H35) Biomedical imaging and signal processing (92C55) Optimal stopping in statistics (62L15) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items

On the uniqueness of solutions for the basis pursuit in the continuum ⋮ Linear inverse problems with nonnegativity constraints: singularity of optimisers ⋮ Statistical model and ML-EM algorithm for emission tomography with known movement ⋮ MLEM ⋮ Predictive risk estimation for the expectation maximization algorithm with Poisson data

Uses Software

• ODL
• GitHub

Cites Work

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